import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import warnings
warnings.filterwarnings('ignore')
print (os.getcwd())
C:\Users\Dell
os.chdir ('C:\\Users\\Dell\\OneDrive\\Desktop\\CAR Price Prediction')
print (os.getcwd())
C:\Users\Dell\OneDrive\Desktop\CAR Price Prediction
data=pd.read_csv("C:\\Users\\Dell\\OneDrive\\Desktop\\excel books\\audi.csv")
display(data)
| model | year | price | transmission | dist_travelled | fuelType | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017 | 12500 | Manual | 15735 | Petrol | 150 | 55.4 | 1.4 |
| 1 | A6 | 2016 | 16500 | Automatic | 36203 | Diesel | 20 | 64.2 | 2.0 |
| 2 | A1 | 2016 | 11000 | Manual | 29946 | Petrol | 30 | 55.4 | 1.4 |
| 3 | A4 | 2017 | 16800 | Automatic | 25952 | Diesel | 145 | 67.3 | 2.0 |
| 4 | A3 | 2019 | 17300 | Manual | 1998 | Petrol | 145 | 49.6 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10663 | A3 | 2020 | 16999 | Manual | 4018 | Petrol | 145 | 49.6 | 1.0 |
| 10664 | A3 | 2020 | 16999 | Manual | 1978 | Petrol | 150 | 49.6 | 1.0 |
| 10665 | A3 | 2020 | 17199 | Manual | 609 | Petrol | 150 | 49.6 | 1.0 |
| 10666 | Q3 | 2017 | 19499 | Automatic | 8646 | Petrol | 150 | 47.9 | 1.4 |
| 10667 | Q3 | 2016 | 15999 | Manual | 11855 | Petrol | 150 | 47.9 | 1.4 |
10668 rows × 9 columns
import pandas_profiling as pp
display(pp.ProfileReport(data))
Summarize dataset: 0%| | 0/5 [00:00<?, ?it/s]
Generate report structure: 0%| | 0/1 [00:00<?, ?it/s]
Render HTML: 0%| | 0/1 [00:00<?, ?it/s]
print(len(data))
10668
print(data.shape)
(10668, 9)
display (data.dtypes )
model object year int64 price int64 transmission object dist_travelled int64 fuelType object tax int64 mpg float64 engineSize float64 dtype: object
display (data.isna().sum() )
model 0 year 0 price 0 transmission 0 dist_travelled 0 fuelType 0 tax 0 mpg 0 engineSize 0 dtype: int64
print (data.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 10668 entries, 0 to 10667 Data columns (total 9 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 model 10668 non-null object 1 year 10668 non-null int64 2 price 10668 non-null int64 3 transmission 10668 non-null object 4 dist_travelled 10668 non-null int64 5 fuelType 10668 non-null object 6 tax 10668 non-null int64 7 mpg 10668 non-null float64 8 engineSize 10668 non-null float64 dtypes: float64(2), int64(4), object(3) memory usage: 750.2+ KB None
display (data.describe ())
| year | price | dist_travelled | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|
| count | 10668.000000 | 10668.000000 | 10668.000000 | 10668.000000 | 10668.000000 | 10668.000000 |
| mean | 2017.100675 | 22896.685039 | 24827.244001 | 126.011436 | 50.770022 | 1.930709 |
| std | 2.167494 | 11714.841888 | 23505.257205 | 67.170294 | 12.949782 | 0.602957 |
| min | 1997.000000 | 1490.000000 | 1.000000 | 0.000000 | 18.900000 | 0.000000 |
| 25% | 2016.000000 | 15130.750000 | 5968.750000 | 125.000000 | 40.900000 | 1.500000 |
| 50% | 2017.000000 | 20200.000000 | 19000.000000 | 145.000000 | 49.600000 | 2.000000 |
| 75% | 2019.000000 | 27990.000000 | 36464.500000 | 145.000000 | 58.900000 | 2.000000 |
| max | 2020.000000 | 145000.000000 | 323000.000000 | 580.000000 | 188.300000 | 6.300000 |
data
| model | year | price | transmission | dist_travelled | fuelType | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017 | 12500 | Manual | 15735 | Petrol | 150 | 55.4 | 1.4 |
| 1 | A6 | 2016 | 16500 | Automatic | 36203 | Diesel | 20 | 64.2 | 2.0 |
| 2 | A1 | 2016 | 11000 | Manual | 29946 | Petrol | 30 | 55.4 | 1.4 |
| 3 | A4 | 2017 | 16800 | Automatic | 25952 | Diesel | 145 | 67.3 | 2.0 |
| 4 | A3 | 2019 | 17300 | Manual | 1998 | Petrol | 145 | 49.6 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10663 | A3 | 2020 | 16999 | Manual | 4018 | Petrol | 145 | 49.6 | 1.0 |
| 10664 | A3 | 2020 | 16999 | Manual | 1978 | Petrol | 150 | 49.6 | 1.0 |
| 10665 | A3 | 2020 | 17199 | Manual | 609 | Petrol | 150 | 49.6 | 1.0 |
| 10666 | Q3 | 2017 | 19499 | Automatic | 8646 | Petrol | 150 | 47.9 | 1.4 |
| 10667 | Q3 | 2016 | 15999 | Manual | 11855 | Petrol | 150 | 47.9 | 1.4 |
10668 rows × 9 columns
data.drop_duplicates(subset=['model','year','price','transmission','dist_travelled','fuelType','tax','mpg','engineSize'],inplace=True,keep='first')
data.shape
(10565, 9)
X = data.iloc[:,[0,1,3,4,5,6,7,8]].values
display (X.shape)
display (X)
(10565, 8)
array([[' A1', 2017, 'Manual', ..., 150, 55.4, 1.4],
[' A6', 2016, 'Automatic', ..., 20, 64.2, 2.0],
[' A1', 2016, 'Manual', ..., 30, 55.4, 1.4],
...,
[' A3', 2020, 'Manual', ..., 150, 49.6, 1.0],
[' Q3', 2017, 'Automatic', ..., 150, 47.9, 1.4],
[' Q3', 2016, 'Manual', ..., 150, 47.9, 1.4]], dtype=object)
data.head()
| model | year | price | transmission | dist_travelled | fuelType | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017 | 12500 | Manual | 15735 | Petrol | 150 | 55.4 | 1.4 |
| 1 | A6 | 2016 | 16500 | Automatic | 36203 | Diesel | 20 | 64.2 | 2.0 |
| 2 | A1 | 2016 | 11000 | Manual | 29946 | Petrol | 30 | 55.4 | 1.4 |
| 3 | A4 | 2017 | 16800 | Automatic | 25952 | Diesel | 145 | 67.3 | 2.0 |
| 4 | A3 | 2019 | 17300 | Manual | 1998 | Petrol | 145 | 49.6 | 1.0 |
Y = data.iloc[:,[2]].values
display (Y.shape)
display (Y)
(10565, 1)
array([[12500],
[16500],
[11000],
...,
[17199],
[19499],
[15999]], dtype=int64)
display(pd.DataFrame(X).head(5))
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017 | Manual | 15735 | Petrol | 150 | 55.4 | 1.4 |
| 1 | A6 | 2016 | Automatic | 36203 | Diesel | 20 | 64.2 | 2.0 |
| 2 | A1 | 2016 | Manual | 29946 | Petrol | 30 | 55.4 | 1.4 |
| 3 | A4 | 2017 | Automatic | 25952 | Diesel | 145 | 67.3 | 2.0 |
| 4 | A3 | 2019 | Manual | 1998 | Petrol | 145 | 49.6 | 1.0 |
from sklearn.preprocessing import LabelEncoder
le1 = LabelEncoder()
X[:,0] = le1.fit_transform(X[:,0])
le2 = LabelEncoder()
X[:,-4] = le2.fit_transform(X[:,-4])
display (X)
array([[0, 2017, 'Manual', ..., 150, 55.4, 1.4],
[5, 2016, 'Automatic', ..., 20, 64.2, 2.0],
[0, 2016, 'Manual', ..., 30, 55.4, 1.4],
...,
[2, 2020, 'Manual', ..., 150, 49.6, 1.0],
[9, 2017, 'Automatic', ..., 150, 47.9, 1.4],
[9, 2016, 'Manual', ..., 150, 47.9, 1.4]], dtype=object)
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
ct = ColumnTransformer(transformers = [('encoder',OneHotEncoder(),[2])],remainder='passthrough')
X = ct.fit_transform(X)
display (X.shape)
display (pd.DataFrame(X))
(10565, 10)
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 1.0 | 0.0 | 0 | 2017 | 15735 | 2 | 150 | 55.4 | 1.4 |
| 1 | 1.0 | 0.0 | 0.0 | 5 | 2016 | 36203 | 0 | 20 | 64.2 | 2.0 |
| 2 | 0.0 | 1.0 | 0.0 | 0 | 2016 | 29946 | 2 | 30 | 55.4 | 1.4 |
| 3 | 1.0 | 0.0 | 0.0 | 3 | 2017 | 25952 | 0 | 145 | 67.3 | 2.0 |
| 4 | 0.0 | 1.0 | 0.0 | 2 | 2019 | 1998 | 2 | 145 | 49.6 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10560 | 0.0 | 1.0 | 0.0 | 2 | 2020 | 4018 | 2 | 145 | 49.6 | 1.0 |
| 10561 | 0.0 | 1.0 | 0.0 | 2 | 2020 | 1978 | 2 | 150 | 49.6 | 1.0 |
| 10562 | 0.0 | 1.0 | 0.0 | 2 | 2020 | 609 | 2 | 150 | 49.6 | 1.0 |
| 10563 | 1.0 | 0.0 | 0.0 | 9 | 2017 | 8646 | 2 | 150 | 47.9 | 1.4 |
| 10564 | 0.0 | 1.0 | 0.0 | 9 | 2016 | 11855 | 2 | 150 | 47.9 | 1.4 |
10565 rows × 10 columns
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X = sc.fit_transform(X)
display (pd.DataFrame(X))
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -0.582997 | 1.203038 | -0.714096 | -1.119276 | -0.039002 | -0.393254 | 1.053589 | 0.357402 | 0.351966 | -0.884062 |
| 1 | 1.715274 | -0.831229 | -0.714096 | -0.158819 | -0.500425 | 0.479662 | -0.951665 | -1.571222 | 1.030836 | 0.111173 |
| 2 | -0.582997 | 1.203038 | -0.714096 | -1.119276 | -0.500425 | 0.212815 | 1.053589 | -1.422867 | 0.351966 | -0.884062 |
| 3 | 1.715274 | -0.831229 | -0.714096 | -0.543002 | -0.039002 | 0.042479 | -0.951665 | 0.283224 | 1.269983 | 0.111173 |
| 4 | -0.582997 | 1.203038 | -0.714096 | -0.735093 | 0.883845 | -0.979108 | 1.053589 | 0.283224 | -0.095471 | -1.547551 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10560 | -0.582997 | 1.203038 | -0.714096 | -0.735093 | 1.345269 | -0.892959 | 1.053589 | 0.283224 | -0.095471 | -1.547551 |
| 10561 | -0.582997 | 1.203038 | -0.714096 | -0.735093 | 1.345269 | -0.979961 | 1.053589 | 0.357402 | -0.095471 | -1.547551 |
| 10562 | -0.582997 | 1.203038 | -0.714096 | -0.735093 | 1.345269 | -1.038346 | 1.053589 | 0.357402 | -0.095471 | -1.547551 |
| 10563 | 1.715274 | -0.831229 | -0.714096 | 0.609547 | -0.039002 | -0.695585 | 1.053589 | 0.357402 | -0.226616 | -0.884062 |
| 10564 | -0.582997 | 1.203038 | -0.714096 | 0.609547 | -0.500425 | -0.558728 | 1.053589 | 0.357402 | -0.226616 | -0.884062 |
10565 rows × 10 columns
from sklearn.model_selection import train_test_split
(X_train,X_test,Y_train,Y_test) = train_test_split(X,Y,test_size=0.2,random_state=0)
print (X_train.shape, Y_train.shape)
print(Y_test.shape)
(8452, 10) (8452, 1) (2113, 1)
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X_train,Y_train)
LinearRegression()
y_pred = reg.predict(X_test)
display (y_pred.shape)
(2113, 1)
print(np.concatenate((y_pred.reshape(len(y_pred),1),Y_test.reshape(len(Y_test),1)),1))
[[31863.18288911 34991. ] [19374.13511826 17299. ] [13295.65796165 11444. ] ... [18373.48622929 17670. ] [20230.20801119 14290. ] [17652.48622929 18990. ]]
from sklearn.metrics import r2_score,mean_absolute_error
print('R2 Score ', r2_score(Y_test, y_pred))
print('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))
R2 Score 0.7941818440937328 Mean Absolute Error 3244.810815892843
y_pred = reg.predict(X)
display (y_pred)
y_pred=y_pred[:2113,0]
array([[14861.39031251],
[20407.69760853],
[13617.39031251],
...,
[19728.17779394],
[21238.75145114],
[16811.25145114]])
result = pd.concat([data,pd.DataFrame(y_pred)],axis=1)
display( result.head())
result.rename(columns={0: 'pred_price'}, inplace=True)
display( result)
| model | year | price | transmission | dist_travelled | fuelType | tax | mpg | engineSize | 0 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017.0 | 12500.0 | Manual | 15735.0 | Petrol | 150.0 | 55.4 | 1.4 | 14861.390313 |
| 1 | A6 | 2016.0 | 16500.0 | Automatic | 36203.0 | Diesel | 20.0 | 64.2 | 2.0 | 20407.697609 |
| 2 | A1 | 2016.0 | 11000.0 | Manual | 29946.0 | Petrol | 30.0 | 55.4 | 1.4 | 13617.390313 |
| 3 | A4 | 2017.0 | 16800.0 | Automatic | 25952.0 | Diesel | 145.0 | 67.3 | 2.0 | 20163.341671 |
| 4 | A3 | 2019.0 | 17300.0 | Manual | 1998.0 | Petrol | 145.0 | 49.6 | 1.0 | 17648.177794 |
| model | year | price | transmission | dist_travelled | fuelType | tax | mpg | engineSize | pred_price | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017.0 | 12500.0 | Manual | 15735.0 | Petrol | 150.0 | 55.4 | 1.4 | 14861.390313 |
| 1 | A6 | 2016.0 | 16500.0 | Automatic | 36203.0 | Diesel | 20.0 | 64.2 | 2.0 | 20407.697609 |
| 2 | A1 | 2016.0 | 11000.0 | Manual | 29946.0 | Petrol | 30.0 | 55.4 | 1.4 | 13617.390313 |
| 3 | A4 | 2017.0 | 16800.0 | Automatic | 25952.0 | Diesel | 145.0 | 67.3 | 2.0 | 20163.341671 |
| 4 | A3 | 2019.0 | 17300.0 | Manual | 1998.0 | Petrol | 145.0 | 49.6 | 1.0 | 17648.177794 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1162 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 20095.841671 |
| 1563 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 7677.746250 |
| 1564 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 22037.069150 |
| 1874 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 21134.659596 |
| 1875 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 13741.697609 |
10578 rows × 10 columns
data
| model | year | price | transmission | dist_travelled | fuelType | tax | mpg | engineSize | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017 | 12500 | Manual | 15735 | Petrol | 150 | 55.4 | 1.4 |
| 1 | A6 | 2016 | 16500 | Automatic | 36203 | Diesel | 20 | 64.2 | 2.0 |
| 2 | A1 | 2016 | 11000 | Manual | 29946 | Petrol | 30 | 55.4 | 1.4 |
| 3 | A4 | 2017 | 16800 | Automatic | 25952 | Diesel | 145 | 67.3 | 2.0 |
| 4 | A3 | 2019 | 17300 | Manual | 1998 | Petrol | 145 | 49.6 | 1.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 10663 | A3 | 2020 | 16999 | Manual | 4018 | Petrol | 145 | 49.6 | 1.0 |
| 10664 | A3 | 2020 | 16999 | Manual | 1978 | Petrol | 150 | 49.6 | 1.0 |
| 10665 | A3 | 2020 | 17199 | Manual | 609 | Petrol | 150 | 49.6 | 1.0 |
| 10666 | Q3 | 2017 | 19499 | Automatic | 8646 | Petrol | 150 | 47.9 | 1.4 |
| 10667 | Q3 | 2016 | 15999 | Manual | 11855 | Petrol | 150 | 47.9 | 1.4 |
10565 rows × 9 columns
actual=data.iloc[:2113,2]
actual
distance=data.iloc[:2113,4]
print(distance)
0 15735
1 36203
2 29946
3 25952
4 1998
...
2121 52595
2122 25000
2123 31292
2124 49652
2125 15016
Name: dist_travelled, Length: 2113, dtype: int64
print(distance.shape)
print(y_pred.shape)
(2113,) (2113,)
%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,y_pred,color='blue')
plt.legend(['Actual price ','Predicted Price'])
plt.show()
from sklearn.ensemble import RandomForestRegressor
regression = RandomForestRegressor(random_state=0)
regression.fit(X_train,Y_train)
display (regression)
RandomForestRegressor(random_state=0)
from sklearn.model_selection import train_test_split
(X_train,X_test,Y_train,Y_test) = train_test_split(X,Y,test_size=0.2,random_state=0)
print (X_train.shape, Y_train.shape)
print (X_test.shape, Y_test.shape)
(8452, 10) (8452, 1) (2113, 10) (2113, 1)
y_pred = regression.predict(X_test)
display (y_pred)
array([34565.81, 16820.73, 11530.84, ..., 18497.45, 17153.97, 18620.66])
result = pd.concat([data,pd.DataFrame(y_pred)],axis=1)
display( result.tail(50))
result.rename(columns={0: 'pred_price'}, inplace=True)
display( result)
| model | year | price | transmission | dist_travelled | fuelType | tax | mpg | engineSize | 0 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 10631 | TT | 2012.0 | 10490.0 | Manual | 24693.0 | Diesel | 165.0 | 51.4 | 2.0 | NaN |
| 10632 | A1 | 2010.0 | 9990.0 | Automatic | 38000.0 | Petrol | 125.0 | 53.3 | 1.4 | NaN |
| 10633 | A4 | 2018.0 | 26891.0 | Automatic | 22414.0 | Petrol | 145.0 | 36.7 | 3.0 | NaN |
| 10634 | Q7 | 2017.0 | 45595.0 | Automatic | 28949.0 | Diesel | 145.0 | 39.2 | 4.0 | NaN |
| 10635 | A3 | 2016.0 | 18000.0 | Automatic | 29494.0 | Petrol | 125.0 | 49.6 | 2.0 | NaN |
| 10636 | A1 | 2013.0 | 9291.0 | Manual | 29382.0 | Petrol | 125.0 | 53.3 | 1.4 | NaN |
| 10637 | A5 | 2017.0 | 21291.0 | Automatic | 29666.0 | Diesel | 30.0 | 65.7 | 2.0 | NaN |
| 10638 | A4 | 2017.0 | 18491.0 | Automatic | 17900.0 | Petrol | 145.0 | 50.4 | 1.4 | NaN |
| 10639 | A6 | 2020.0 | 28000.0 | Automatic | 2511.0 | Diesel | 145.0 | 47.9 | 2.0 | NaN |
| 10640 | Q5 | 2020.0 | 37000.0 | Automatic | 1436.0 | Petrol | 145.0 | 32.1 | 2.0 | NaN |
| 10641 | A5 | 2020.0 | 25000.0 | Automatic | 751.0 | Petrol | 145.0 | 40.4 | 2.0 | NaN |
| 10642 | Q5 | 2019.0 | 33000.0 | Automatic | 5207.0 | Diesel | 145.0 | 38.2 | 2.0 | NaN |
| 10643 | A4 | 2019.0 | 30000.0 | Automatic | 9900.0 | Diesel | 145.0 | 49.6 | 2.0 | NaN |
| 10644 | A5 | 2019.0 | 25000.0 | Automatic | 8571.0 | Diesel | 145.0 | 46.3 | 2.0 | NaN |
| 10645 | A1 | 2016.0 | 10999.0 | Manual | 22150.0 | Diesel | 0.0 | 76.3 | 1.6 | NaN |
| 10646 | A1 | 2016.0 | 12380.0 | Manual | 40119.0 | Petrol | 30.0 | 55.4 | 1.4 | NaN |
| 10647 | A3 | 2015.0 | 21000.0 | Automatic | 12084.0 | Petrol | 205.0 | 39.8 | 2.0 | NaN |
| 10648 | RS6 | 2016.0 | 49990.0 | Automatic | 24000.0 | Petrol | 325.0 | 29.4 | 4.0 | NaN |
| 10649 | A3 | 2009.0 | 3750.0 | Manual | 120000.0 | Diesel | 145.0 | 53.3 | 2.0 | NaN |
| 10650 | A4 | 2011.0 | 6995.0 | Manual | 88000.0 | Diesel | 30.0 | 61.4 | 2.0 | NaN |
| 10651 | A3 | 2011.0 | 9695.0 | Manual | 32300.0 | Petrol | 235.0 | 39.2 | 2.0 | NaN |
| 10652 | A1 | 2014.0 | 9995.0 | Manual | 54000.0 | Petrol | 30.0 | 55.4 | 1.2 | NaN |
| 10653 | A3 | 2017.0 | 12995.0 | Manual | 23820.0 | Petrol | 145.0 | 60.1 | 1.0 | NaN |
| 10654 | A3 | 2016.0 | 16495.0 | Semi-Auto | 46600.0 | Diesel | 125.0 | 57.6 | 2.0 | NaN |
| 10655 | S4 | 2018.0 | 29995.0 | Automatic | 29000.0 | Petrol | 150.0 | 35.8 | 3.0 | NaN |
| 10656 | A3 | 2016.0 | 15495.0 | Semi-Auto | 52500.0 | Hybrid | 0.0 | 176.6 | 1.4 | NaN |
| 10657 | A4 | 2016.0 | 20995.0 | Semi-Auto | 23700.0 | Diesel | 30.0 | 61.4 | 2.0 | NaN |
| 10658 | A3 | 2016.0 | 14995.0 | Manual | 39750.0 | Petrol | 30.0 | 57.6 | 1.4 | NaN |
| 10659 | A6 | 2018.0 | 27995.0 | Semi-Auto | 27500.0 | Petrol | 150.0 | 39.8 | 2.0 | NaN |
| 10660 | A4 | 2011.0 | 9995.0 | Automatic | 78000.0 | Diesel | 305.0 | 39.8 | 3.0 | NaN |
| 10661 | A4 | 2011.0 | 6995.0 | Manual | 95000.0 | Diesel | 145.0 | 53.3 | 2.0 | NaN |
| 10662 | A3 | 2013.0 | 12695.0 | Manual | 31500.0 | Petrol | 125.0 | 53.3 | 1.4 | NaN |
| 10663 | A3 | 2020.0 | 16999.0 | Manual | 4018.0 | Petrol | 145.0 | 49.6 | 1.0 | NaN |
| 10664 | A3 | 2020.0 | 16999.0 | Manual | 1978.0 | Petrol | 150.0 | 49.6 | 1.0 | NaN |
| 10665 | A3 | 2020.0 | 17199.0 | Manual | 609.0 | Petrol | 150.0 | 49.6 | 1.0 | NaN |
| 10666 | Q3 | 2017.0 | 19499.0 | Automatic | 8646.0 | Petrol | 150.0 | 47.9 | 1.4 | NaN |
| 10667 | Q3 | 2016.0 | 15999.0 | Manual | 11855.0 | Petrol | 150.0 | 47.9 | 1.4 | NaN |
| 273 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 21563.460 |
| 764 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 20608.700 |
| 784 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 31939.952 |
| 967 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 119217.800 |
| 990 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6965.660 |
| 1133 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 12045.820 |
| 1137 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 17817.460 |
| 1146 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 21198.890 |
| 1162 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 44255.550 |
| 1563 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 32483.820 |
| 1564 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 33188.750 |
| 1874 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 12127.800 |
| 1875 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 25382.800 |
| model | year | price | transmission | dist_travelled | fuelType | tax | mpg | engineSize | pred_price | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | A1 | 2017.0 | 12500.0 | Manual | 15735.0 | Petrol | 150.0 | 55.4 | 1.4 | 34565.810000 |
| 1 | A6 | 2016.0 | 16500.0 | Automatic | 36203.0 | Diesel | 20.0 | 64.2 | 2.0 | 16820.730000 |
| 2 | A1 | 2016.0 | 11000.0 | Manual | 29946.0 | Petrol | 30.0 | 55.4 | 1.4 | 11530.840000 |
| 3 | A4 | 2017.0 | 16800.0 | Automatic | 25952.0 | Diesel | 145.0 | 67.3 | 2.0 | 24245.476667 |
| 4 | A3 | 2019.0 | 17300.0 | Manual | 1998.0 | Petrol | 145.0 | 49.6 | 1.0 | 15256.480000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1162 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 44255.550000 |
| 1563 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 32483.820000 |
| 1564 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 33188.750000 |
| 1874 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 12127.800000 |
| 1875 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 25382.800000 |
10578 rows × 10 columns
from sklearn.metrics import r2_score,mean_absolute_error
print ('R2 Score ', r2_score(Y_test, y_pred))
print ('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))
R2 Score 0.9587306545197221 Mean Absolute Error 1517.4627789258122
%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,y_pred,color='blue')
plt.legend(['Actual price ','Predicted Price'])
plt.show()
from sklearn.ensemble import ExtraTreesRegressor
ET_Model=ExtraTreesRegressor(n_estimators=120)
ET_Model.fit(X_train,Y_train)
y_predict=ET_Model.predict(X_test)
import numpy as np
from sklearn.metrics import r2_score,mean_absolute_error
print('R2 Score :', r2_score(Y_test, y_predict))
print ('Mean Absolute Error:', mean_absolute_error(Y_test,y_predict))
R2 Score : 0.9625678491519055 Mean Absolute Error: 1517.800980044171
'''y_pred = reg.predict(X)
display (y_pred)
result = pd.concat([data,pd.DataFrame(y_pred)],axis=1).head()
display( result)'''
'y_pred = reg.predict(X)\ndisplay (y_pred)\nresult = pd.concat([data,pd.DataFrame(y_pred)],axis=1).head()\ndisplay( result)'
%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,Y_test,color='red')
plt.scatter(distance,y_predict,color='blue')
plt.legend(['Actual price ','Predicted Price'])
plt.show()
### Randomized search CV
from sklearn.model_selection import RandomizedSearchCV
n_estimators = [int(x) for x in np.linspace(start = 80, stop = 1500, num = 10)]
max_features = ['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(6, 45, num = 5)]
min_samples_split = [2, 5, 10, 15, 100]
min_samples_leaf = [1, 2, 5, 10]
rand_grid={'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf}
rf=RandomForestRegressor()
rCV=RandomizedSearchCV(estimator=rf,param_distributions=rand_grid,scoring='neg_mean_squared_error',n_iter=3,cv=3,random_state=42, n_jobs = 1)
rCV.fit(X_train,Y_train)
RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
param_distributions={'max_depth': [6, 15, 25, 35, 45],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [1, 2, 5, 10],
'min_samples_split': [2, 5, 10, 15,
100],
'n_estimators': [80, 237, 395, 553, 711,
868, 1026, 1184, 1342,
1500]},
random_state=42, scoring='neg_mean_squared_error')
rf_pred=rCV.predict(X_test)
display (rf_pred)
array([34363.68578869, 16651.55211445, 11707.03009425, ...,
18883.21945933, 16724.75648133, 18457.28272006])
rf_pred=rCV.predict(X_test)
display (rf_pred)
array([34363.68578869, 16651.55211445, 11707.03009425, ...,
18883.21945933, 16724.75648133, 18457.28272006])
from sklearn.metrics import mean_absolute_error,mean_squared_error
print('MAE',mean_absolute_error(Y_test,rf_pred))
print('MSE',mean_squared_error(Y_test,rf_pred))
MAE 1505.2773269897075 MSE 6100595.4818421
display (r2_score(Y_test,rf_pred))
0.9580830027921906
%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,rf_pred,color='blue')
plt.legend(['Actual price ','Predicted Price'])
plt.show()
from catboost import CatBoostRegressor
cat=CatBoostRegressor()
print (cat.fit(X_train,Y_train))
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cat_pred=cat.predict(X_test)
display (cat_pred)
array([34403.2885362 , 18068.56325027, 11918.19730173, ...,
18621.48599089, 16917.10863635, 18220.58207119])
display (r2_score(Y_test,cat_pred))
print('MAE',mean_absolute_error(Y_test,cat_pred))
print('MSE', mean_squared_error(Y_test,cat_pred))
0.9621533622246573
MAE 1440.7720299246405 MSE 5508195.786795388
plt.plot(cat_pred,label='pred')
plt.plot(Y_test,label='Actual')
plt.legend('a','b')
plt.show()
%matplotlib inline
plt.figure(figsize=(18,5))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,cat_pred,color='blue')
plt.title('ACTUAL PRICE VS PREDICTED PRICE')
plt.legend(['Actual price ','Predicted Price'])
plt.show()
'''from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error'''
'from sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.metrics import mean_squared_error'
#X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)
#knn_regressor = KNeighborsRegressor(n_neighbors=4)
#knn_regressor.fit(X_train, y_train)
#y_pred = knn_regressor.predict(X_test)
#print(y_pred)
#mse = mean_squared_error(y_test, y_pred)
#print(f'Mean Squared Error: {mse}')
#from sklearn.metrics import r2_score,mean_absolute_error
#print ('R2 Score ', r2_score(Y_test, y_pred))
#print ('Mean Absolute Error', mean_absolute_error(Y_test,y_pred))
'''%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,y_pred,color='blue')
plt.legend(['Actual price ','Predicted Price'])
plt.show()'''
## knn regressor is not good
"%matplotlib inline\nplt.figure(figsize=(10, 6))\nplt.xlabel('dist_travlled')\nplt.ylabel('price')\nplt.scatter(distance,actual,color='red')\nplt.scatter(distance,y_pred,color='blue')\n\nplt.legend(['Actual price ','Predicted Price'])\nplt.show()"
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error, r2_score
decision_tree_regressor = DecisionTreeRegressor(random_state=42)
decision_tree_regressor.fit(X_train, Y_train)
DecisionTreeRegressor(random_state=42)
y_pred = decision_tree_regressor.predict(X_test)
mse = mean_squared_error(Y_test, y_pred)
r2 = r2_score(Y_test, y_pred)
print('MAE',mean_absolute_error(Y_test,y_pred))
MAE 1921.797128884682
print(r2)
0.9337260347094475
%matplotlib inline
plt.figure(figsize=(10, 6))
plt.xlabel('dist_travlled')
plt.ylabel('price')
plt.scatter(distance,actual,color='red')
plt.scatter(distance,y_pred,color='blue')
plt.legend(['Actual price ','Predicted Price'])
plt.show()